"""
The LAMBADA dataset: Word prediction requiring a broad discourse context∗
https://arxiv.org/pdf/1606.06031.pdf

LAMBADA is a dataset to evaluate the capabilities of computational models for text
understanding by means of a word prediction task. LAMBADA is a collection of narrative
passages sharing the characteristic that human subjects are able to guess their last
word if they are exposed to the whole passage, but not if they only see the last
sentence preceding the target word. To succeed on LAMBADA, computational models
cannot simply rely on local context, but must be able to keep track of information
in the broader discourse.

Homepage: https://zenodo.org/record/2630551#.X4Xzn5NKjUI
"""
import inspect
from .tasks_utils import Task, rf
from .tasks_utils  import mean, perplexity
from .local_datasets import lambada

_CITATION = """
@misc{
    author={Paperno, Denis and Kruszewski, Germán and Lazaridou, Angeliki and Pham, Quan Ngoc and Bernardi, Raffaella and Pezzelle, Sandro and Baroni, Marco and Boleda, Gemma and Fernández, Raquel},
    title={The LAMBADA dataset},
    DOI={10.5281/zenodo.2630551},
    publisher={Zenodo},
    year={2016},
    month={Aug}
}
"""



def preprocess(text):
    text = text.replace("“", '"')
    text = text.replace("”", '"')
    text = text.replace("''", '"')
    text = text.replace("``", '"')
    return '\n' + text.strip()


class LAMBADA(Task):
    VERSION = 0
    DATASET_PATH = inspect.getfile(lambada.Lambada)


    def has_training_docs(self):
        return False

    def has_validation_docs(self):
        return True

    def has_test_docs(self):
        return False

    def training_docs(self):
        pass

    def validation_docs(self):
        return self.dataset["validation"]

    def test_docs(self):
        pass

    def doc_to_text(self, doc):
        return preprocess(doc["text"].strip()).rsplit(" ", 1)[0]

    def should_decontaminate(self):
        return True

    def doc_to_decontamination_query(self, doc):
        return doc["text"]

    def doc_to_target(self, doc):
        return " " + doc["text"].rsplit(" ", 1)[1]

    def construct_requests(self, doc, ctx):
        ll, is_greedy = rf.loglikelihood(ctx, self.doc_to_target(doc))

        return ll, is_greedy

    def process_results(self, doc, results):
        ll, is_greedy = results

        return {"ppl": ll, "acc": int(is_greedy)}

    def aggregation(self):
        return {"ppl": perplexity, "acc": mean}

    def higher_is_better(self):
        return {"ppl": False, "acc": True}